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Contrastive Linearized Laplace: Loss Geometry, Valid Repairs, and Reliable Cores
While contrastive learning is widely used to learn deterministic representations, it does not inherently provide estimates of uncertainty. Post-hoc uncertainty estimation methods, such as the Linearized Laplace approximation (LLA), offer a way to quantify uncertainty without retraining the encoder.
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